The traditional analytical biosensor instruments are relatively bulky, expensive, and not easy to handle, thus their applications are largely limited in resource-limited settings. The recent development of microfluidic lab-on-a-chip (LOC) technology has provided a possible solution to miniaturize the conventional biosensing system, yet other accessory devices to detect, readout, analyze, transfer, and display results are still required. With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health. Based on the common analytical methods, this paper reviews the recent development of four types of smartphone based analytical biosensory systems at the POC, i.e., smartphone-based microscopic imaging, colorimetric, electrochemical, and electrochemiluminescence biosensor. The different bio-sensing strategies and analytical performance together with future perspectives are discussed.

The lattice stability and phonon response of Ti3C2Tx MXene at high pressure are important for understanding its mechanical and thermal properties fully. Here, we use in situ high hydrostatic pressure X-ray diffraction (XRD) and Raman spectroscopy to study the lattice deformation and phonon behavior of Ti3C2Tx MXene. XRD spectra indicate that no phase transformation occurs up to the pressure of 26.7 GPa. The elastic constant along a lattice parameter was calculated to be 378 GPa. In the Raman spectra obtained at high-pressure, the out-of-plane phonon modes (A1g at ~ 210, ~ 504, and ~ 711 cm−1) exhibit monotonic blueshifts with increasing pressure. The Grüneisen parameters of these three modes were calculated to be 1.08, 1.16, and 0.29, respectively. These results enrich the basic property data of Ti3C2Tx MXene and would benefit the further understanding of this novel material.

A comprehensive review of recent progress in inertial microfluidics with special focus on multistage cell separation and detection devices in addition to straight and curved channels.

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

Foodborne bacteria, inducing outbreaks of infection or poisoning, have posed great threats to food safety. Potentiometric sensors can identify bacteria levels in food by measuring medium's pH changes. However, most of these sensors face the limitation of low sensitivity and high cost. In this paper, we developed a high-sensitivity ion-sensitive field-effect transistor sensor. It is small sized, cost-efficient, and can be massively fabricated in a standard 65-nm complementary metal–oxide–semiconductor process. A subthreshold pH-to-time-to-voltage conversion scheme was proposed to improve the sensitivity. Furthermore, design parameters, such as chemical sensing area, transistor size, and discharging time, were optimized to enhance the performance. The intrinsic sensitivity of passivation membrane was calculated as 33.2 mV/pH. It was amplified to 123.8 mV/pH with a 0.01-pH resolution, which greatly exceeded 6.3 mV/pH observed in a traditional source-follower based readout structure. The sensing system was applied to Escherichia coli (E. coli) detection with densities ranging from 14 to 140 cfu/mL. Compared to the conventional direct plate counting method (24 h), more efficient sixfold smaller screening time (4 h) was achieved to differentiate samples’ E. coli levels. The demonstrated portable, time-saving, and low-cost prescreen system has great potential for food safety detection.